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Land use (LU) monitoring and mapping from remote sensing (RS) data are relevant topics in Earth observation (EO). LU monitoring requires powerful/robust classification approaches able to provide reliable identifications in decision-making applications, even over challenging areas. The European Common Agricultural Policy (CAP) promotes the use of remote sensing for LU monitoring in Europe useful in the compliance control of the subsidy payments. In this context, the present study proposes a novel classification chain to identify ten land uses, namely: forest (FOR), pasture with trees (TRE), pasture with shrubs (SHR), pastureland (PAS), vineyard (VIN), arable land (ARL), fruit trees (FRU), nut trees (NUT), citrus (CIT), and olive grove (OLI), over three areas in the Valencian Autonomous Region (Spain) where smallholding farming predominates. The approach exploits the multitemporal information provided by Sentinel-2 data using a novel spatial strategy specifically designed to deal with heterogeneous agricultural areas. More precisely, we implemented a deep learning algorithm based on bidirectional recurrent neural networks to account for complex temporal dynamics. The classification results over the three areas showed accuracies higher than 95.5% over validation sets composed of in-situ checks never used during the training process. The proposed methodology outperformed standard approaches that not consider the spatial variability of the training samples, and revealed very good agreement concerning the Land Parcel Identification System (LPIS) of Spain. In addition, the developed chain proposes the novelty of using the kernel normalized difference vegetation index (kNDVI) as a predictor in a LU classification processing chain. Including the kNDVI instead of the traditional NDVI outperformed the classification accuracy in all metrics and classes. Ultimately, the obtained classifications were used for assessing the 2020 farmers' declarations in the three study areas. The declarations showed a level of agreement concerning the proposed approach near 99%. [ABSTRACT FROM AUTHOR]

The termination or interruption of agro-forestry practices for a long period gradually results in abandoned land. Abandoned land parcels do not match the requirements to access to the basic payment of the European Common Agricultural Policy (CAP). Therefore, the identification of those parcels is key in order to return fair subsidies to farmers. In this context, the present work proposes a methodology to detect abandoned crops in the Valencian Community (Spain) from remote sensing data. The approach is based on the assessment of multitemporal Sentinel-2 images and derived spectral indices, which are used as predictors for training machine learning and deep learning classifiers. Several classification scenarios, including both abandoned and active parcels, were evaluated. The best results ( 98.2 % overall accuracy) were obtained when a bi-directional Long Short Term Memory (BiLSTM) network was trained with a multitemporal dataset composed of twelve reflectance time series, and a derived bare soil spectral index (BSI). In this scenario we were able to effectively distinguish abandoned crops from active ones. The results revealed Sentinel-2 features are well suited for land use identification including abandoned lands, and open the possibility of implementing this type of remote sensing based methodology into the CAP payments supervision. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for deriving an agreement map between the Spanish Land Parcel Information System (LPIS), also known as SIGPAC, and a classification map obtained from multitemporal Sentinel-1 and Sentinel-2 data. The study area comprises the province of València (Spain). The approach exploits predictions and class probabilities obtained from an ensemble method of decision trees (boosting trees). The overall accuracy reaches 91.18% when using only Sentinel-2 data and increases up to 93.96% when Sentinel-1 data are added in the training process. Blending both Setninel-1 and Sentinel-2 data causes a remarkable classification improvement ranging from 3.6 to 8.7 percentage points over shrubs, forest, and pasture with trees, which are the most confusing classes in the optical domain as demonstrated by a spectral separability analysis. The derived agreement map is built upon combining per pixel classifications, their probabilities, and the Spanish LPIS. This map can be exploited into the decision-making chain for subsidies payment to cope with the 2020+ European Common Agricultural Policy (CAP). [ABSTRACT FROM AUTHOR]